Low Resolution Information Also Matters: Learning Multi-Resolution Representations for Person Re-Identification

• URL: http://arxiv.org/abs/2105.12684v1
• Date: Wed, 26 May 2021 16:54:56 GMT
• Title: Low Resolution Information Also Matters: Learning Multi-Resolution Representations for Person Re-Identification
• Authors: Guoqing Zhang, Yuhao Chen, Weisi Lin, Arun Chandran, Xuan Jing
• Abstract summary: Cross-resolution person re-ID aims to match person images captured from non-overlapped cameras. emphtextbfMulti-Resolution textbfRepresentations textbfJoint textbfLearning (textbfMRJL) Our method consists of a Resolution Reconstruction Network (RRN) and a Dual Feature Fusion Network (DFFN)
• Score: 37.01666917620271
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a prevailing task in video surveillance and forensics field, person re-identification (re-ID) aims to match person images captured from non-overlapped cameras. In unconstrained scenarios, person images often suffer from the resolution mismatch problem, i.e., \emph{Cross-Resolution Person Re-ID}. To overcome this problem, most existing methods restore low resolution (LR) images to high resolution (HR) by super-resolution (SR). However, they only focus on the HR feature extraction and ignore the valid information from original LR images. In this work, we explore the influence of resolutions on feature extraction and develop a novel method for cross-resolution person re-ID called \emph{\textbf{M}ulti-Resolution \textbf{R}epresentations \textbf{J}oint \textbf{L}earning} (\textbf{MRJL}). Our method consists of a Resolution Reconstruction Network (RRN) and a Dual Feature Fusion Network (DFFN). The RRN uses an input image to construct a HR version and a LR version with an encoder and two decoders, while the DFFN adopts a dual-branch structure to generate person representations from multi-resolution images. Comprehensive experiments on five benchmarks verify the superiority of the proposed MRJL over the relevent state-of-the-art methods.

Related papers

• Reference-based Image and Video Super-Resolution via C2-Matching [100.0808130445653]
  We propose C2-Matching, which performs explicit robust matching crossing transformation and resolution. C2-Matching significantly outperforms state of the arts on the standard CUFED5 benchmark. We also extend C2-Matching to Reference-based Video Super-Resolution task, where an image taken in a similar scene serves as the HR reference image.
  arXiv  Detail & Related papers  (2022-12-19T16:15:02Z)
• Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network [15.102899995465041]
  We propose a novel face super-resolution method, namely Semantic guided Generative Adversarial Face Ultra-Resolution Network (SEGA-FURN) The proposed network is composed of a novel semantic encoder that has the ability to capture the embedded semantics to guide adversarial learning and a novel generator that uses a hierarchical architecture named Residual in Internal Block (RIDB) Experiments on large face datasets have proved that the proposed method can achieve superior super-resolution results and significantly outperform other state-of-the-art methods in both qualitative and quantitative comparisons.
  arXiv  Detail & Related papers  (2022-11-18T23:16:57Z)
• Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification [49.57112924976762]
  Cross-resolution person re-identification problem aims to match low-resolution (LR) query identity images against high resolution (HR) gallery images. It is a challenging and practical problem since the query images often suffer from resolution degradation due to the different capturing conditions from real-world cameras. This paper explores an alternative SR-free paradigm to directly compare HR and LR images via a dynamic metric, which is adaptive to the resolution of a query image.
  arXiv  Detail & Related papers  (2022-07-09T03:49:51Z)
• Memory-augmented Deep Unfolding Network for Guided Image Super-resolution [67.83489239124557]
  Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of a HR image. Previous model-based methods mainly takes the entire image as a whole, and assume the prior distribution between the HR target image and the HR guidance image. We propose a maximal a posterior (MAP) estimation model for GISR with two types of prior on the HR target image.
  arXiv  Detail & Related papers  (2022-02-12T15:37:13Z)
• Resolution based Feature Distillation for Cross Resolution Person Re-Identification [17.86505685442293]
  Person re-identification (re-id) aims to retrieve images of same identities across different camera views. Resolution mismatch occurs due to varying distances between person of interest and cameras. We propose a Resolution based Feature Distillation (RFD) approach to overcome the problem of multiple resolutions.
  arXiv  Detail & Related papers  (2021-09-16T11:07:59Z)
• Robust Reference-based Super-Resolution via C2-Matching [77.51610726936657]
  Super-Resolution (Ref-SR) has recently emerged as a promising paradigm to enhance a low-resolution (LR) input image by introducing an additional high-resolution (HR) reference image. Existing Ref-SR methods mostly rely on implicit correspondence matching to borrow HR textures from reference images to compensate for the information loss in input images. We propose C2-Matching, which produces explicit robust matching crossing transformation and resolution.
  arXiv  Detail & Related papers  (2021-06-03T16:40:36Z)
• Deep High-Resolution Representation Learning for Cross-Resolution Person Re-identification [22.104449922937338]
  Person re-identification (re-ID) tackles the problem of matching person images with the same identity from different cameras. We propose a Deep High-Resolution Pseudo-Siamese Framework (PS-HRNet) to solve the problem. Our proposed PS-HRNet improves 3.4%, 6.2%, 2.5%,1.1% and 4.2% at Rank-1 on MLR-Market-1501, MLR-CUHK03, MLR-VIPeR, MLR-DukeMTMC-reID, and CAVIAR datasets.
  arXiv  Detail & Related papers  (2021-05-25T07:45:38Z)
• PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models [77.32079593577821]
  PULSE (Photo Upsampling via Latent Space Exploration) generates high-resolution, realistic images at resolutions previously unseen in the literature. Our method outperforms state-of-the-art methods in perceptual quality at higher resolutions and scale factors than previously possible.
  arXiv  Detail & Related papers  (2020-03-08T16:44:31Z)
• Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond [59.149653740463435]
  Person re-identification (re-ID) aims at matching images of the same person across camera views. Due to varying distances between cameras and persons of interest, resolution mismatch can be expected. We propose a novel generative adversarial network to address cross-resolution person re-ID.
  arXiv  Detail & Related papers  (2020-02-19T07:21:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.